Within the field of data visualization is the area of dynamically-linked graphs or views. Under the linked-view architecture, views (graphs, tables, etc.) share a common data model and each view contains a depiction of the data observations in the data model. Each data observation has data values and properties associated with it. Whenever a change to a data value or property occurs, the data model notifies all views that a change has taken place. The view then updates its display.
One data observation property common to most data model implementations is the observation selection property. This property is usually designed to hold a Boolean value that indicates whether the observation has been selected in one (or more) of the views associated with the data model.
Under the linked-view paradigm, the purpose of observation selection is to visually focus attention on specific data points across multiple views simultaneously. Selected observations are typically differentiated from unselected observations in a view using highlighting, color, or size. Selecting an observation in one view results in that observation being displayed as selected in all the data model views. Such a technique has been termed the global selection approach.
FIGS. 1 and 2 illustrate the traditional global observation selection approach. The plots (30, 32, 34) in these figures are generated from a data set containing information and statistics about former professional baseball players. Each figure contains three plots: a histogram 30 of the players salaries; a bar chart 32 showing the number of players in the data set by their fielding position; and a scatter plot 34 of the number of At Bats versus the number of RBIs for each player.
Because the displays (30, 32, 34) employ the traditional global observation selection approach, selections made in one plot are automatically visualized in all plots. The visualization is achieved by the data model notifying the views of selection property changes. After the views retrieve the selection property changes from the data model, the views update their displays.
For example, in FIG. 1, the salary histogram 30 was used to select observations involving players with higher salaries. The selected observations are those shown within region 40. These observations are highlighted in the bar chart 32 as shown by the patterned portions (e.g., 42) of the bars in the bar chart 32. The points appearing in the scatter plot 34 are players whose salaries correspond to the observations selected in the histogram 30.
In FIG. 2, the bars (50, 52, 54) representing the fielding positions for the outfielder positions (center field, left field, and right field) are selected. The previous selections from the salary histogram 30 have been replaced with fielding position selections in all the plots (30, 32, 34). For example, histogram 30 shows the salaries for the positions selected in bar chart 32. The display of bar chart 32 has been modified to highlight the selected positions; and plot 34 shows points related to the selected positions.
The usefulness of this traditional approach diminishes as the size and dimension of data sets become larger. This is because the observation selection process is limited in its ability to reduce or focus data selections across the views sufficiently to aid the data investigation process.